This paper shows that sub-dictionaries sampled from a coherent dictionary using a coherence rejective Poisson sampling model are well-conditioned with high probability, as long as their expected size…

This paper analyzes the statistical and computational limits of learning bounded linear operators between Sobolev spaces from noisy data, and constructs a finite-resolution blockwise least-squares est…

This paper introduces the Distributed Truncated Spectral Transform (DTST) for Fourier Neural Operators (FNOs), achieving significant speedups in distributed computing.

This paper simplifies the proof of the bound on the target dimension for reducing the dimension of high-dimensional polygonal curves using random projections, extending it to various distance measures…

This paper identifies scattering network architectures that maximize separation capacity for data with low intrinsic dimension by characterizing and bounding the separation capacity of general feature…

This paper proves conditions for efficiently approximating expectations of certain functions with respect to standard Gaussian or symmetric exponential probability measures.

The paper analyzes the structured CVP distance on the log-unit lattice of cyclotomic fields, significantly reducing the conjectured CDPR factor for the ML-KEM cryptosystem from exponential to sub-poly…

This paper improves transformer attention in character-level language modeling by applying FFT-based spectral preprocessing to learned query-key projections.

This paper provides the first algorithmic separation between constant-depth and logarithmic-depth networks, identifying a class of Boolean functions that logarithmic-depth networks can learn efficient…

The paper formalizes the problem of representation identifiability in supervised learning, showing that a representation property is identifiable if and only if it is constant across all possible fact…

The paper describes how to compute singular value soft-thresholding using matrix polar decomposition for faster GPU processing.

This paper introduces a structural theorem for the sparsifiability of real-valued codes, which generalizes both combinatorial and continuous notions of sparsification.